Scenario-based Evaluation of Prediction Models for Automated Vehicles
van de Molengraft, R.
To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most appropriate for every situation. Currently, assessment of prediction models is often performed over a set of trajectories without distinction of the type of movement they capture, resulting in the inability to determine the suitability of each model for different situations. In this work we illustrate how standardized evaluation methods result in wrong conclusions regarding a model's predictive capabilities, preventing a clear assessment of prediction models and potentially leading to dangerous on-road situations. We argue that following evaluation practices in safety assessment for AVs, assessment of prediction models should be performed in a scenario-based fashion. To encourage scenario-based assessment of prediction models and illustrate the dangers of improper assessment, we categorize trajectories of the Waymo Open Motion dataset according to the type of movement they capture. Next, three different models are thoroughly evaluated for different trajectory types and prediction horizons. Results show that common evaluation methods are insufficient and the assessment should be performed depending on the application in which the model will operate.
To reference this document use:
Intelligent vehicle highway systems
Institute of Electrical and Electronics Engineers Inc.
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, 8 October 2022 through 12 October 2022, 2227-2233